diff --git a/crates/xserv-model/src/bin/sink-attn-check.rs b/crates/xserv-model/src/bin/sink-attn-check.rs
new file mode 100644
index 0000000..433611f
--- /dev/null
+++ b/crates/xserv-model/src/bin/sink-attn-check.rs
@@ -0,0 +1,57 @@
+//! Unit-test decode_attention_sink (GPU) against a CPU reference on small random
+//! inputs. Isolates the sink/window attention kernel from the rest of gpt-oss.
+use half::bf16;
+use xserv_kernels::decode_attention_sink;
+use xserv_tensor::{Device, Tensor};
+
+fn main() {
+    let (n_heads, n_kv, head_dim, kv_len) = (8usize, 2usize, 4usize, 3usize);
+    let scale = (head_dim as f32).powf(-0.5);
+    let n_rep = n_heads / n_kv;
+
+    // deterministic pseudo-random fill
+    let mut seed = 12345u64;
+    let mut rnd = || { seed = seed.wrapping_mul(6364136223846793005).wrapping_add(1); ((seed >> 33) as f32 / (1u64 << 31) as f32) - 1.0 };
+
+    let q: Vec<f32> = (0..n_heads * head_dim).map(|_| rnd()).collect();          // [H,1,D]
+    let k: Vec<f32> = (0..n_kv * kv_len * head_dim).map(|_| rnd()).collect();    // [Hkv,kv,D]
+    let v: Vec<f32> = (0..n_kv * kv_len * head_dim).map(|_| rnd()).collect();
+    let sinks: Vec<f32> = (0..n_heads).map(|_| rnd()).collect();
+
+    let qb: Vec<bf16> = q.iter().map(|&x| bf16::from_f32(x)).collect();
+    let kb: Vec<bf16> = k.iter().map(|&x| bf16::from_f32(x)).collect();
+    let vb: Vec<bf16> = v.iter().map(|&x| bf16::from_f32(x)).collect();
+
+    let qt = Tensor::from_slice(&qb, &[1, n_heads, 1, head_dim]).to_device(Device::Cuda(0));
+    let kt = Tensor::from_slice(&kb, &[1, n_kv, kv_len, head_dim]).to_device(Device::Cuda(0));
+    let vt = Tensor::from_slice(&vb, &[1, n_kv, kv_len, head_dim]).to_device(Device::Cuda(0));
+    let st = Tensor::from_slice(&sinks, &[n_heads]).to_device(Device::Cuda(0)); // f32
+
+    let out = decode_attention_sink(&qt, &kt, &vt, &st, scale, 0); // [1,H,1,D]
+    let outh = out.to_device(Device::Cpu);
+    let og = outh.as_slice::<bf16>();
+
+    // CPU reference: for each q head, softmax over [s_0..s_{kv-1}, sink], drop sink, weight V.
+    let mut max_diff = 0f32;
+    for h in 0..n_heads {
+        let kv = h / n_rep;
+        let mut s = vec![0f32; kv_len];
+        let mut m = sinks[h];
+        for j in 0..kv_len {
+            let mut dot = 0f32;
+            for d in 0..head_dim { dot += q[h * head_dim + d] * k[(kv * kv_len + j) * head_dim + d]; }
+            s[j] = dot * scale;
+            if s[j] > m { m = s[j]; }
+        }
+        let mut denom = (sinks[h] - m).exp();
+        for j in 0..kv_len { denom += (s[j] - m).exp(); }
+        for d in 0..head_dim {
+            let mut acc = 0f32;
+            for j in 0..kv_len { acc += (s[j] - m).exp() / denom * v[(kv * kv_len + j) * head_dim + d]; }
+            let got = og[h * head_dim + d].to_f32();
+            max_diff = max_diff.max((got - acc).abs());
+        }
+    }
+    println!("decode_attention_sink vs CPU ref: max_abs_diff = {max_diff:.5}");
+    println!("{}", if max_diff < 0.05 { "SINK_KERNEL_OK" } else { "SINK_KERNEL_MISMATCH" });
+}